134

CHAPTER 6

SEGMENTATION OF EXUDATES

Like hemorrhages, exudates are also the pathological features in

DR. The segmentation of exudates also greatly helps in automatic screening

system. The exudates are precipitations of plasma protein in the retinal region

and appear as bright, reflective, white or cream-colored lesions on the retinal

image. The exudates are a developed stage from microaneurysms and

hemorrhages. The vision loss is inevitable, if it is ignored.

The segmentation of the exudates is carried out using various

clustering techniques and the results are presented and discussed elaborately

in this chapter. The images from the standard database are used for testing,

validating and measure the performance of the proposed technique. The

relative merits of each clustering technique have been presented.

The segmentation of exudates will be successful only when the OD

is eliminated from the retinal image as it has more or less equal brightness.

Detection of the OD center and OD boundary using vessel-direction matched

filter and masking of OD have been explained in the Chapter 4. In this

process, the images with OD detection and elimination by masking

techniques are used as input images. OD masked image is subjected to further

processing to segment the exudates. As exudates are the bright region,

Hussain et al (2010) and Giancardo et al (2011) applied the thresholding

technique to segment the exudates.

135

In th

resh

oldi

ng te

chni

que,

the

thre

shol

d va

lue

of th

e im

age

is s

et a

s

one

or m

ore

than

one

. If

thre

shol

d va

lue

is s

et a

s on

e, t

he i

mag

e w

ill b

e

divi

ded

into

two

regi

ons

as e

xpla

ined

in C

hapt

er 4

, Sec

tion

4.1.

2. I

n re

tinal

imag

es,

the

thre

shol

d va

lues

can

not

be f

ixed

eas

ily a

s th

ey h

ave

diff

eren

t

inte

nsity

lev

els.

Furth

er,

it w

ill n

ot b

e un

iform

. H

ence

, th

e th

resh

oldi

ng

tech

niqu

e is

not

abl

e to

yie

ld s

atis

fact

ory

resu

lts.

In o

rder

to

achi

eve

the

segm

enta

tion

proc

ess s

ucce

ssfu

lly, v

ario

us c

lust

erin

g te

chni

ques

like

Fuz

zy C

Mea

ns

(FC

M),

K

mea

ns

clus

terin

g,

com

bina

tion

of

K

mea

ns

with

mor

phol

ogy

and

fuzz

y ar

e us

ed i

n se

gmen

tatio

n pr

oces

s. Th

e pr

opos

ed

clus

terin

g te

chni

ques

are

app

lied

over

the

OD

mas

ked

imag

es a

fter c

onve

rting

them

into

gra

y sc

ale

imag

es. T

he e

ntire

pro

cess

is s

chem

atic

ally

illu

stra

ted

as

bloc

k di

agra

m in

Fig

ure

6.1.

Figu

re 6

.1 B

lock

dia

gram

for

exud

ates

segm

enta

tion

OD

mas

ked

Retin

al Im

age

RG

B to

gra

y K m

eans

and

m

orph

olog

y Fu

zzy

C M

eans

Fu

zzy

Exud

ates

Seg

men

tatio

n an

d Pa

ram

etric

Mea

sure

s

Km

eans

136

6.1

CL

UST

ER

ING

TE

CH

NIQ

UE

S

The

clus

terin

g te

chni

que

uses

int

ensi

ty r

elat

ions

hip

amon

g th

e pi

xels

of

an i

mag

e. I

t cla

ssifi

es th

e pi

xels

int

o di

ffer

ent g

roup

s ba

sed

on it

s in

tens

ity. I

n a

parti

cula

r gr

oup,

sim

ilarit

y m

easu

rem

ents

am

ong

the

pixe

ls a

re

carr

ied

out a

nd d

epen

ding

on

thei

r val

ue, t

he p

ixel

s will

be

clus

tere

d. A

mon

g th

e va

rious

clu

ster

s, th

e si

mila

rity

leve

l w

ill b

e va

ryin

g w

here

as i

n th

e pa

rticu

lar

clus

ter

the

sim

ilarit

y va

lue

will

be

mor

e or

less

the

sam

e w

ithou

t m

uch

varia

tion.

A s

tand

ard

proc

edur

e fo

r as

sign

ing

a pi

xel

to th

e cl

uste

r is

ba

sed

on th

e m

ean

valu

e of

the

pixe

ls p

rese

nt in

the

clus

ter.

Ach

ievi

ng s

uch

a gr

oupi

ng r

equi

res

a si

mila

rity

met

ric th

at in

volv

es th

e in

put

vect

ors

and

its

valu

e re

flect

s the

ir si

mila

rity

Jeya

ram

an (2

012)

.

In o

rder

to

achi

eve

bette

r re

sult,

the

RG

B c

olor

im

ages

are

co

nver

ted

into

gra

y sc

ale

imag

es a

nd re

duce

d in

to 2

56 X

256

siz

e be

fore

the

appl

icat

ion

of th

e cl

uste

ring

tech

niqu

e. T

his

proc

ess

will

ena

ble

one

to o

btai

n re

sults

in

a sh

ort

time

as t

hese

pro

cess

es a

re i

tera

tive

ones

. C

onve

rsio

n of

R

GB

col

or s

pace

to g

ray

scal

e is

exp

lain

ed in

Cha

pter

4, S

ectio

n 4.

1.1.

1. T

he

vario

us c

lust

erin

g te

chni

ques

are

dis

cuss

ed i

n th

e fo

llow

ing

sect

ions

in

a se

quen

tial m

anne

r.

6.2

FUZ

ZY

C M

EA

NS

CL

UST

ER

ING

(FC

M)

It is

a d

ata

clus

terin

g al

gorit

hm a

nd it

clu

ster

s th

e pi

xel b

ased

on

its

mem

bers

hip

grad

e in

to d

iffer

ent c

lust

ers.

The

vario

us s

teps

inv

olve

d in

this

137

(a)

(b)

(c)

(d)

138

The images with various clusters contain useful information based

on their grouping. From the above resultant image, the first cluster shows the

exudates clearly compared with the other clusters as the intensity level of the

exudates matches with the first cluster. In most of the tested images, the

exudates are segmented in first cluster only. However, it is not necessary that

in all the images the exudates will be segmented in the first cluster. The

segmentation process basically depends on the intensity of the exudates

corresponding to the clusters. In other clusters, the features like blood vessels

and optic disc are also segmented. The 1st cluster image is considered as the

segmented output of exudates. The algorithm is tested with images of STARE

database and the parametric measures are carried out. The results are

presented in Table 6.1 with image identification number, TP, FP, FN, TN, SE,

SPE, ACC and processing time in seconds.

Table 6.1 Parametric measures of the fuzzy c means method

FUZZY C MEANS Sl.No. Image ID Number of Pixels Percentage Time

(Sec.)TP FP FN TN SE SPE ACC1 im0017 24708 3076 8543 387173 74 99 97 03.642 im0049 14317 2158 8864 398161 62 99 97 06.603 im0052 5278 95047 2617 320558 67 77 77 09.704 im0064 24606 1290 11321 386283 68 100 97 06.975 im0096 6325 1079 1958 414138 76 100 99 10.596 im0106 3195 5619 2305 412381 58 99 98 11.557 im0124 2598 25279 1738 393885 60 94 94 10.168 im0171 60053 1426 9843 352178 86 100 97 11.829 im0172 35949 1131 5657 380763 86 100 98 12.19

10 im0176 50755 1970 14357 356418 78 99 96 08.7711 im0178 29297 2084 17760 374359 62 99 95 10.7712 im0227 8012 22016 819 392653 91 95 95 11.6413 im0246 49912 3331 18491 351766 73 99 95 06.2314 im0308 66604 1478 19631 335787 77 100 95 12.6915 im0313 27137 516 12085 383762 69 100 97 09.17

Mean value 73 97 95 09.50

139

From the results, it is inferred that the mean values of SE, SPE,

ACC and time in seconds are estimated as 73%, 97%, 95% and 9.5 sec

respectively. The FCM method has the highest specificity of 97% and

accuracy of 95%. However, the sensitivity is relatively lower as 73%. Hence,

it is attempted to increase the sensitivity using various other algorithms. In

continuation of this algorithm, K means clustering technique is adopted and

the various steps involved are briefed in the next section.

6.3 K MEANS CLUSTERING ALGORITHM

It is one of the clustering techniques that classify a given data set to

a certain fixed number of clusters based on the similarity of the pixels or

group of pixels. The fixed number of clusters is assumed as ‘K’. Unlike in the

previous technique, membership grade of the pixel is not considered in this

algorithm. Instead, it classifies each pixel in a group with the closest mean

distance between the centroid and the pixel. It is an iterative procedure and

this algorithm clusters the data iteratively by computing a mean intensity for

each group and the various steps involved in this algorithm are given below.

Step 1 : Provide the input data and number of clusters

Step 2 : Calculate the cluster centroids based on assumed initial

value

Step 3 : Calculate the distance of each pixel from class centroid

Step 4 : Group pixels into k clusters based on minimal distance

from centroids

Step 5 : Calculate new centroid for each cluster

Step 6 : Classify into groups based on new centroid and distance

Step 7 : Test if any centroid changes its position.

140

Step 8 : If there are changes repeat step 3- 8, else go to step 9

Step 9 : End

The above steps are illustrated as a flow diagram in Figure 6.3.

Figure 6.3 Flow chart for K means clustering technique

In this method, if there are ‘K’ number of clusters, the number of

centroids or cluster centers are also ‘K’ corresponding to each cluster. The

centroid for a cluster is based on the range of intensity values present in a

particular cluster. Usually, it is assumed as the mean value of the variation

range corresponding to the intensity levels. At the beginning stage, these

centroids are fixed at suitable positions and they are iterated towards the exact

location in the process.

No

Yes

Number of Cluster K Number of Cluster K

Start

End

Distance between the objects and Centroids

Centroid

Grouping Based on minimum distance

Object Move to the

Group

Number of Cluster K

141

The pixels present in the original image are transferred to different clusters based on their intensity value. This algorithm predicts a cluster center in each group so that a cost function (objective function) of dissimilarity (distance) measure is minimized. Euclidean distance is a measure of distance between the pixel intensity and the cluster center. It is used for calculating the error function. The equation for error function in terms of euclidean distance for the pixels contained in cluster is given in Equation (6.1). Finally, this algorithm aims at minimizing a squared error objective function (J).

2)(

1 1j

ji

k

j

n

i

cxJ (6.1)

where,

2)(j

ji cx - Euclidean distance measure between a data point

‘ ix ’belonging to cluster j and its cluster centre ‘ jc ’.

If a pixel has a value corresponding to the nearest value of the centroid of the particular cluster, it is transferred to that cluster. This process continues till all the pixels are grouped into any one of the clusters that are assumed to be initial clusters. Further, the centroids of the clusters are iterated towards the better values within the cluster. The above step is repeated until no pixel is moving from one cluster to another. When the step reaches this point, if the clusters are stable, the clustering process stops.

Usually, exudates are the high intensity pixel regions in the retinal image. Based on this property, this clustering technique is applied to segment the exudates in the retinal images. OD masked image is adopted for the K means clustering algorithm and is separated into five clusters based on the intensities in the image. Resultant clusters after the application of the K means clustering on STARE database image with an identification number im0049 are shown in Figures.6.4 (a) to (f). Figures 6.4 (a),(b),(c),(d) and (e) show the resultant images of five different clusters with various intensity levels.

142

(a) (b)

(c) (d)

(e) (f)

Figure 6.4 Resultant output of K means clustering technique (a) output of cluster1 (b) output of cluster 2 (c) output of cluster 3 (d) output of cluster 4 (e) output of cluster 5 (f) post processed output

143

The resultant images of the clusters shown in Figures 6.4 (d) and

(e) have segmented the exudates in comparison with other clusters. However,

Figure 6.4 (d) has segmented even small exudates compared to Figure 6.4 (e).

As a result, Figure 6.4 (d) is suitable for further processing to fill the image

regions and holes (contour of bright pixels contained darker region within the

boundary). The filling of the holes is achieved by filling bright pixels in the

holes.

The image with holes filled is shown in Figure 6.4 (f) and the

image with the segmented exudates is considered as output using K means

clustering technique. Similar to FCM, K means clustering algorithm is

implemented in the exudates images in the STARE database and the predicted

SE , SPE, ACC and the processing time of are tabulated in Table 6.2.

Table 6.2 Parametric measures of the K means clustering method

K MEANS Sl.No. Image ID Number of Pixels Percentage Time

(Sec.)TP FP FN TN SE SPE ACC1 im0017 28884 8878 4367 381371 87 98 97 1.77 2 im0049 15090 4001 5091 399318 75 99 98 1.78 3 im0052 6181 435 1314 415570 82 100 100 2.82 4 im0064 33447 20776 2480 366797 93 95 95 3.33 5 im0096 6745 1675 1538 413542 81 100 99 3.85 6 im0106 7188 19456 707 396149 91 95 95 3.02 7 im0124 2666 50172 72 370590 97 88 88 3.07 8 im0171 65132 5256 4764 348348 93 99 98 3.07 9 im0172 40671 13883 935 368011 98 96 97 3.00

10 im0176 61949 48929 3163 309459 95 86 88 3.04 11 im0178 43070 18860 3987 357583 92 95 95 3.16 12 im0227 6440 2758 2391 411911 73 99 99 3.09 13 im0246 64426 60502 3977 294595 94 83 85 3.07 14 im0308 76597 8224 9638 329041 89 98 96 3.12 15 im0313 33735 7711 5487 376567 86 98 97 2.60

Mean value 88 95 95 2.92

144

In this clustering technique, the mean values of the parametric

measures are 88%, 95%, 95% and 2.92 sec corresponding to SE, SPE, ACC

and the processing time respectively. It is to be noticed that the SE has

increased from 73% to 88% with the marginal loss of SPE from 97% to 95%

whereas the accuracy remains the same in comparison with the previous

technique. Further, in view of improving the above parametric measures,

efforts have been taken with other post-processing operations like

morphology that is briefed in the following section.

6.4 COMBINATION OF K MEANS AND MORPHOLOGY

The resultant image from the previous section after application of

K means clustering is taken for morphological operations to enhance the

sensitivity. In morphological operations, the segmented pixels are dilated

using structuring elements to acquire the other smaller left out details in the

cluster. The considered structuring elements are with parameters as length 3

and angle 900 and 00. The parameter ‘Length’ is the distance between the

centers of the structuring element members at opposite ends of the line, and

the angle of the line is measured in a counterclockwise direction from the

horizontal axis. The above two structuring elements are applied for dilation

one after another. The steps involved in the morphological operations are

detailed in Chapter 3 Section 3.4. The resultant image after the dilation

is shown in Figure 6.5. (a). Further, the holes are filled as explained in

the previous section to obtain better segmentation and this is shown in

Figure 6.5 (b).

145

(a) (b)Figure 6.5 Post processed K means clustered image (a) Dilated image

using structuring elements (b) The post processed image with holes filled

The combination of K means and morphological operations is

performed on the images available in STARE database. The parametric

measures are performed and the results are presented in Table 6.3.

Table 6.3 Parametric measures of the combination of K means and

morphology method

K MEANS AND MORPHOLOGY Sl. No. Image ID Number of Pixels Percentage Time

(Sec.) TP FP FN TN SE SPE ACC1 im0017 31726 19351 1525 370898 95 95 95 2.77 2 im0049 21270 13870 1911 386449 92 97 96 3.30 3 im0052 6867 1252 628 414753 92 100 100 2.85 4 im0064 34968 31372 959 356201 97 92 92 3.04 5 im0096 7654 5443 629 409774 92 99 99 2.81 6 im0106 7889 50983 6 364622 100 88 88 2.59 7 im0124 2738 61052 0 359710 100 85 86 2.83 8 im0171 66534 8619 3362 344985 95 98 97 2.91 9 im0172 40995 13779 611 368115 99 96 97 3.33 10 im0176 63643 58444 1469 299944 98 84 86 3.88 11 im0178 46281 33101 776 343342 98 91 92 2.77 12 im0227 8615 130543 216 284126 98 69 69 2.64 13 im0246 67148 66966 1255 288131 98 81 84 2.60 14 im0308 78824 11178 7411 326087 91 97 96 2.91 15 im0313 35062 15291 4160 368987 89 96 95 3.12

Mean value 96 91 91 2.96

146

The mean values of the parametric measures of SE, SPE, ACC and

the processing time are 96%, 91%, 91% and 2.92 sec respectively. It is to be

noted that the SE has increased in comparison with the previous two

algorithms as 96% as against 73% and 88%. On the other hand, the specificity

is higher in the FCM as 97% and it gradually decreases in K means algorithm

as 95% and in the present algorithm as 91%. It is because in dilation

operation the nearby pixels are associated with the exudates that reduce the

specificity. With regard to ACC, both the previous algorithms of FCM and K

means have 95% and the present algorithm yields 91%. This is also due to the

reason mentioned above. The computational time for the FCM is

comparatively higher as 9.50 seconds and it is more or less the same as 2.92

and 2.96 for the later algorithms. It is general expectation that the

segmentation algorithm should have higher specificity and accuracy and the

search process is extended for other techniques so as to improve the above

parameters.

In the above algorithms, the intensity levels of the pixels are

divided into five clusters and the pixel with a particular value just below or

above the prescribed centroid of the cluster is grouped into the clusters

accordingly. This approach is based on the crisp value of the pixels, whereas

in the fuzzy logic, the membership function is used to group the pixels based

on the membership value corresponding to intensity. Hence, there is a

possibility of improving the parametric measures of the segmentation process

using fuzzy logic. The Fuzzy Inference System (FIS) is applied to achieve the

above expectation and the steps involved are discussed in the next section.

6.5 FUZZY INFERENCE SYSTEM

In this technique, the pixels are grouped based on the membership

function corresponding to intensity values. The various important

147

terminologies used in the Fuzzy logic like Fuzzy Set (FS), Fuzzy membership

functions (FMF) and Fuzzy Rules (FR) are briefed below.

Fuzzy Set : If ‘S’ is a collection of objects, then ‘x’ in the fuzzy set

‘FS’ is the set of defined elements.

FS = {(x, mf (x)) x S} (6.2)

where, ‘mf (x)’ is the membership function of ‘x’ in ‘S’ which varies from 0

to 1.

The fuzzy inference system consists of five different steps that are

detailed below with its functioning.

Step 1: Define the inputs and outputs for the fuzzy inference

system: The input and output set of variables are predetermined and are

called as fuzzy variables. The fuzzy value of the pixel is determined with the

rules framed using the above input and output set of variables. The range of

all possible values of fuzzy variables is called universe of discourse.

Step 2: Set up fuzzy membership functions for input: Fuzzification

is the process of converting crisp values into fuzzy values. Membership

functions are used to determine the selected value and its grouping and its

value varies from 0 to 1. There are different shapes of membership

functions: triangular, trapezoidal, Gaussian and bell-shaped etc. In this

present work, triangular membership function is used. Based on this

function, the input crisp values are converted into fuzzy values.

Step 3: Set up fuzzy membership functions for the output: It is

developed in a similar way as discussed in Step 2 for the set of output values

also.

148

Step 4: Create a fuzzy rule base: The rules are framed so as to

obtain the desired output. The set of input and output fuzzy variables are used

to frame the rules in the form of IF-THEN statements. The membership

values are used to select the rules to predict the output. The membership

function and rules are adjusted to achieve the desired performance.

Step 5: Set of membership values for fuzzy outputs are reconverted

to crisp output so as to use the image for further application, and this process

is called defuzzification. The popular defuzzification methods are center of

gravity method, average of maxima method and midpoint of maxima method.

In this process for defuzzification, the centroid method is adopted.

Based on the above steps, two types of fuzzy models proposed by

Mamdani and Sugeno Jang et al (1997) are used by various researchers in

different image processing systems. The models developed by them have a

flexibility to select the number of input and output variables, set of rules and

various types of membership functions. In the present work, the model

proposed by Mamdani is considered in the segmentation process of exudates.

The model consists of a fuzzifier, fuzzy rule base, an inference engine and a

defuzzifier.

The above stages are coded in MATLAB environment and the

different fuzzy logic tool boxes available in the software are used for the

segmentation process. The Fuzzy Logic Toolbox is a collection of builtin

functions in MATLAB. It provides tools to create and edit fuzzy inference

systems within the framework of MATLAB. There are five primary

Graphical User Interface (GUI) tools for building, editing and observing

fuzzy inference systems. In the fuzzy logic toolbox, namely the fuzzy

inference system or FIS editor, the membership function editor, the rule

editor, the rule viewer and the surface viewer are available in the software.

149

6.5.1 Fuzzy Clustering for Exudates Segmentation

Fuzzy model proposed by Mamdani is used to segment the

exudates. OD masked image is used as an input for fuzzy system. Input color

image is converted into gray scale image. The gray scale image is adopted for

segmenting the exudates. The initial step is to fuzzify the inputs to be

achieved by triangular membership function. The concept of the triangular

function with its lower limit ‘a’ and upper limit ‘b’ and a value ‘m’,

where a < m < b is shown in Figure 6.6. The equation for predicting the

membership value xA of the corresponding input value of the ‘x’ is given

in Equation (6.3).

Figure 6.6 Triangular membership function

bx

bxmmbxb

mxaamax

ax

xA

,0

,

,

,0

(6.3)

Input x

Mem

bers

hip

valu

e

150

The input gray values are in the range of 0 to 255. The entire range

is fuzzified by five overlapped MFs such as mf1, mf2, mf3, mf4 and mf5. The

input gray scale value is divided into five groups using five membership

functions as shown below in the format as

MF='mf1':'trimf',[a m b].

MF1 = 'mf1':'trimf',[-94.5 8.12 80.25]

MF2 = 'mf2':'trimf',[26.8 85.7 152.72]

MF3 = 'mf3':'trimf',[132 189 249.57]

MF4 = 'mf4':'trimf',[78.2 134 204.87]

MF5 = 'mf5':'trimf',[197 252.95 281]

The above five membership functions in the range of 0 to 255 are

shown graphically representing ‘a’, ‘m’ and ‘b’ on x – axis and degree of

membership values that ranges from 0 to 1 on y-axis as shown in Figure 6.7.

For the particular input value of ‘x’, it is possible to predict membership value

using the triangular function.

0 50 100 150 200 250

0

0.2

0.4

0.6

0.8

1

Gray values

mf1 mf2 mf3mf4 mf5

Figure 6.7 Fuzzified input using the triangular membership function

151

In the similar way, the triangular membership function is used for

output set of variables. It is divided into five ranges not necessarily equal such

as mf1, mf2, mf3, mf4 and mf5 using the triangular membership functions as

shown in Figures 6.8 (a) to (e). The range of gray scale value is divided into

five groups and assigned as output variables as shown below in the format

similar to the input membership function.

output1 = 'mf1':'trimf', [-102 0 50.46]

output2 = 'mf2':'trimf', [53.2 80.25 107]

output3 = 'mf3':'trimf', [101.25 130 149]

output4 = 'mf4':'trimf', [150 173.71 201]

output5 = 'mf5':'trimf', [197 223.15 251]

Figures 6.8 (a), (b), (c), (d) and (e) show the triangular membership

function of the output1, output2, output3, output4 and output 5.

0 50 100 150 200 250

0

0.2

0.4

0.6

0.8

1

Gray values

mf1

output 1

0 50 100 150 200 250

0

0.2

0.4

0.6

0.8

1

Gray values

mf2

output 2

(a) (b)

Figure 6.8 Membership functions for output variables

152

0 50 100 150 200 250

0

0.2

0.4

0.6

0.8

1

Gray values

mf3

output 3

0 50 100 150 200 250

0

0.2

0.4

0.6

0.8

1

Gray values

mf4

output 4

(c) (d)

0 50 100 150 200 250

0

0.2

0.4

0.6

0.8

1

Gray values

mf5

output 5

(e)

Figure 6.8 (Continued)

Further, fuzzy rules are framed and executed. Rules for the

proposed fuzzy system are given in Table 6.4. These rules are created in the

Mamdani fuzzy models available in the MATLAB.

153

Table 6.4 Rules for the fuzzy inference systems

Input

Output

mf1 mf2 mf3 mf4 mf5

mf1 1 0 0 0 0

mf2 0 1 0 0 0

mf3 0 0 1 0 0

mf4 0 0 0 1 0

mf5 0 0 0 0 1

Output of the Mamdani fuzzy models is converted into crisp value

using the defuzzification process. Defuzzification refers to the way a crisp

value is extracted from the fuzzy set as a representative value. In this work,

the mostly used method centroid of area is adopted for defuzzification.

The fuzzy algorithm is employed over a STARE database image

with identification number im0049 and the results at various clusters are

shown in Figures 6.9 (a) to (f). Figures 6.9 (a) to (e) correspond to five

clusters and Figure 6.9 (f) shows the final segmented output of exudates

derived from the result of 3rd cluster.

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(a) (b)

(c) (d)

(e) (f)Figure 6.9 Resultant output of fuzzy clustering technique (a) output of

cluster1 (b) output of cluster 2 (c) output of cluster 3 (d) output of cluster 4 (e) output of cluster 5 (f) Final output from cluster 3

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Figures 6.9 (b) and (c) contain information about the exudates. The

output of the 3rd cluster corresponds to Figure 6.9(c).This picture shows the

segmented exudates in a better manner than in other clusters. Hence, this

output image is converted into binary images and they are tested for the

parametric measures and it is tabulated in Table 6.5.

Table 6.5 Parametric measures of the fuzzy inference system

FUZZY

Sl.

No.Image ID

Number of Pixels Percentage Time

(Sec.)TP FP FN TN SE SPE ACC

1 im0017 29966 13710 3285 376539 90 96 96 13.64

2 im0049 16569 3169 2112 401650 89 99 99 9.22

3 im0052 6492 1027 1003 414978 87 100 100 21.68

4 im0064 34264 30570 1663 357003 95 92 92 17.87

5 im0096 6991 983 1792 413734 80 100 99 15.27

6 im0106 7367 21431 528 394174 93 95 95 17.89

7 im0124 2543 25256 195 395506 93 94 94 14.62

8 im0171 60720 1673 9176 351931 87 100 97 19.34

9 im0172 40571 13145 1035 368749 98 97 97 15.28

10 im0176 60345 50485 4767 307903 93 86 87 25.95

11 im0178 43328 23076 3729 353367 92 94 94 15.95

12 im0227 8358 107445 473 307224 95 74 75 14.52

13 im0246 63139 39916 5264 315181 92 89 89 14.80

14 im0308 76603 5876 9632 331389 89 98 96 14.72

15 im0313 35209 30895 4013 353383 90 92 92 17.41

Mean value 91 94 93 17

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The mean value of the SE, SPE, ACC and the processing time of

the fuzzy clustering techniques are 91%, 94%, 93% and 17 sec respectively.

The algorithm consumes highest computational time and has yielded the

mean values for all the parameters above 90%. The results of all the

algorithms are discussed elaborately in the next section. The resultant images

after the application of various exudates segmentation algorithms are shown

in Figure 6.10.

15

Image ID

Input Image Fuzzy c means K means Kmeans morphalogy Fuzzy

Im0037

Im0045

Im0096

Figure 6.10 Resultant images after the application of various exudates segmentation algorithms

Im0178

Im0171

Figure 6.10 (Continued)

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6.6 RESULTS AND DISCUSSION

The segmentation of exudates has been performed on retinal

images using fuzzy c means, K means, K means with morphology and fuzzy

algorithms. The performance measures of the resultant images are carried out

and the results are discussed in the respective sections. The results of each

performance measures with respect to different algorithms are briefed in this

section to illustrate the capabilities of each algorithm.

The sensitivity of the resultant images when the above mentioned

algorithms are applied independently is consolidated and presented in

Table 6.6 with mean values of different images.

Table 6.6 Sensitivity value of the various segmentation algorithms

Sensitivity Image ID FCM K means K morph Fuzzy

im0017 74 87 95 90im0049 62 75 92 89im0052 67 82 92 87im0064 68 93 97 95im0096 76 81 92 80im0106 58 91 100 93im0124 60 97 100 93im0171 86 93 95 87im0172 86 98 99 98im0176 78 95 98 93im0178 62 92 98 92im0227 91 73 98 95im0246 73 94 98 92im0308 77 89 91 89im0313 69 86 89 90

Mean value 73 88 96 91

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It is evident from the table that the K means & morphological

operations yielded the best result of 96 %. The mean sensitivity value for the

FCM is 73 % and it is very less compared to the other methods. K means and

Fuzzy have 88% and 91% respectively. Sensitivity values in Table 6.6 are

plotted graphically and are shown in Figure 6.11 for better illustration.

Comparision Chart - Sensitivity

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images from STARE database

FCM Kmeans Kmorph Fuzzy

Figure 6.11 Comparison chart for the sensitivity values

The above Figure shows that the K means & morphology algorithm

out performs in all the selected images with respect to sensitivity. The

performance of fuzzy is next to the K means & morphology. The FCM has no

consistency and varies widely for different images.

In a similar manner, the specificity values of different images

corresponding to different algorithms are tabulated in Table 6.7 and the same

is also presented in a graphical form in Figure 6.12.

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Table 6.7 Specificity value of the various segmentation algorithms

Specificity Image ID FCM K means K morph Fuzzyim0017 99 98 95 96im0049 99 99 97 99im0052 77 100 100 100im0064 100 95 92 92im0096 100 100 99 100im0106 99 95 88 95im0124 94 88 85 94im0171 100 99 98 100im0172 100 96 96 97im0176 99 86 84 86im0178 99 95 91 94im0227 95 99 69 74im0246 99 83 81 89im0308 100 98 97 98im0313 100 98 96 92

Mean value 97 95 91 94

Comparision Chart - Specificity

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images from STARE database

FCM Kmeans Kmorph Fuzzy

Figure 6.12 Comparison chart for the specificity values

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It is inferred that the mean specificity values of all methods yield

the values above 90% and highest is achieved by FCM. The lowest is yielded

by K means and morphology. It is noted from Figure 6.11 that with respect to

specificity, no algorithm outperforms in all the images like sensitivity.

K means & morphology is varying to the larger extent with respect to various

images and it is found to be less consistent.

The parametric measure of accuracy with respect to each image

using various algorithms is tabulated in Table 6.8 and graphical

representation is shown in Figure 6.13.

Table 6.8 Accuracy value of the various segmentation algorithms

Accuracy

Image ID FCM K means Kmorph Fuzzy

im0017 97 97 95 96

im0049 97 98 96 99

im0052 77 100 100 100

im0064 97 95 92 92

im0096 99 99 99 99

im0106 98 95 88 95

im0124 94 88 86 94

im0171 97 98 97 97

im0172 98 97 97 97

im0176 96 88 86 87

im0178 95 95 92 94

im0227 95 99 69 75

im0246 95 85 84 89

im0308 95 96 96 96

im0313 97 97 95 92

Mean value 95 95 91 93

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Comparision Chart - Accuracy

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images fromSTARE database

FCM Kmeans Kmorph Fuzzy

Figure 6.13 Comparison chart for the accuracy values

In the case of accuracy, FCM and K means algorithms yielded the

highest values of 95% and the other two algorithms have also yielded the

nearer values without deviating much. Like specificity, in accuracy also not a

single algorithm outperforms in all the selected images as Figure 6.13 shows.

The processing time is compared for all the algorithms and the

results are tabulated in Table 6.9 and plotted graphically in Figure 6.14.

Table 6.9 Processing time of the various segmentation algorithms

Time

Image ID FCM Kmeans Kmorph Fuzzy

im0017 03.64 1.77 2.77 13.64

im0049 06.60 1.78 3.30 09.22

im0052 09.70 2.82 2.85 21.68

im0064 06.97 3.33 3.04 17.87

im0096 10.59 3.85 2.81 15.27

im0106 11.55 3.02 2.59 17.89

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Table 6.9 (Continued)

im0124 10.16 3.07 2.83 14.62

im0171 11.82 3.07 2.91 19.34

im0172 12.19 3.00 3.33 15.28

im0176 08.77 3.04 3.88 25.95

im0178 10.77 3.16 2.77 15.95

im0227 11.64 3.09 2.64 14.52

im0246 06.23 3.07 2.60 14.80

im0308 12.69 3.12 2.91 14.72

im0313 09.17 2.60 3.12 17.41

Mean value 09.50 2.92 2.96 17

Comparision Chart - Processing Time

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images from STARE database

FCM Kmeans Kmorph Fuzzy

Figure 6.14 Comparison chart for the processing time

It is inferred from the table and graph that the processing time for

the fuzzy is higher than the other three methods. Both the K means and

K means & morphology have lowest processing time.

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The mean values of the results of all the tested images with regard

to different algorithms are presented in Table 6.10

Table 6.10 Mean values of results of different algorithms

Sl.No. Algorithms Mean values

SE SPE ACC Time in sec.

1 FCM 73 97 95 09.50

2 K means 88 95 95 02.92

3 K & morph 96 91 91 02.96

4 Fuzzy 91 94 93 17.00

The above table indicates that the SPE and ACC have increased in

fuzzy technique in comparison with K mean & morphological operations.

However, these values are less with respect to FCM method and it claims the

advantage of increase in sensitivity with marginal loss of SPE and ACC.

6.7 SUMMARY

The segmentation of exudates has been performed using four

different clustering algorithms like Fuzzy C means, K means, K means and

morphology and fuzzy. All the algorithms are briefed and tested on images of

STARE database and the results are presented. The algorithms are subjected

to parametric measures and the output of the different algorithms is discussed

with the aid of tables and charts. The comparative study of all the algorithms

has been presented and discussed. Suggestion has been made on the better

algorithm in segmentation process of exudates with the evidence of mean

sensitivity, specificity and accuracy. The time consumption by the various

algorithms is also presented which could be a point of research for future

researchers to expand on.